Loading report..

Highlight Samples

Regex mode off

    Rename Samples

    Click here for bulk input.

    Paste two columns of a tab-delimited table here (eg. from Excel).

    First column should be the old name, second column the new name.

    Regex mode off

      Show / Hide Samples

      Regex mode off

        Export Plots

        px
        px
        X

        Download the raw data used to create the plots in this report below:

        Note that additional data was saved in multiqc_data when this report was generated.


        Choose Plots

        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        Save Settings

        You can save the toolbox settings for this report to the browser.


        Load Settings

        Choose a saved report profile from the dropdown box below:

        About MultiQC

        This report was generated using MultiQC, version 1.10

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/ewels/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        Report generated on 2022-03-03, 10:24 based on data in: C:\Users\ChengXin\PycharmProjects\untitled\pmultiqc_test\test_data\PXD000001


        quantms

        quantms is an module to show the pipeline performance.

        Experimental Design

        This plot shows the Proteomics Experimental Design

        This plot shows the Proteomics Experimental Design. You can see details about it in https://abibuilder.informatik.uni-tuebingen.de/archive/openms/Documentation/release/latest/html/classOpenMS_1_1ExperimentalDesign.html

        Showing 1/1 rows and 6/6 columns.
        Spectra FileFraction_GroupFractionLabelSampleMSstats_ConditionMSstats_BioReplicate
        TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzML
        1
        1
        1|2|3|4|5|6
        1|2|3|4|5|6
        SP=Yeast;CT=protein;AC=P00924;QY=10|SP=BOVIN;CT=protein;AC=P02769;QY=1|SP=RABIT;CT=protein;AC=P00489;QY=2|SP=BOVIN;CT=protein;AC=P62894;QY=1SP=Yeast;CT=protein;AC=P00924;QY=5|SP=BOVIN;CT=protein;AC=P02769;QY=2.5|SP=RABIT;CT=protein;AC=P00489;QY=2|SP=BOVIN;CT=protein;AC=P62894;QY=1SP=Yeast;CT=protein;AC=P00924;QY=2.5|SP=BOVIN;CT=protein;AC=P02769;QY=5|SP=RABIT;CT=protein;AC=P00489;QY=2|SP=BOVIN;CT=protein;AC=P62894;QY=1SP=Yeast;CT=protein;AC=P00924;QY=1|SP=BOVIN;CT=protein;AC=P02769;QY=10|SP=RABIT;CT=protein;AC=P00489;QY=2|SP=BOVIN;CT=protein;AC=P62894;QY=1SP=Yeast;CT=protein;AC=P00924;QY=2.5|SP=BOVIN;CT=protein;AC=P02769;QY=5|SP=RABIT;CT=protein;AC=P00489;QY=1|SP=BOVIN;CT=protein;AC=P62894;QY=1SP=Yeast;CT=protein;AC=P00924;QY=10|SP=BOVIN;CT=protein;AC=P02769;QY=1|SP=RABIT;CT=protein;AC=P00489;QY=1|SP=BOVIN;CT=protein;AC=P62894;QY=2
        1|2|3|4|5|6

        HeatMap

        This plot shows the pipeline performance overview

        This plot shows the pipeline performance overview. Some metrics are calculated.

        • Heatmap score[Contaminants]: as fraction of summed intensity with 0 = sample full of contaminants; 1 = no contaminants
        • Heatmap score[Pep Intensity (>23.0)]: Linear scale of the median intensity reaching the threshold, i.e. reaching 2^21 of 2^23 gives score 0.25.
        • Heatmap score[Charge]: Deviation of the charge 2 proportion from a representative Raw file (median). For typtic digests, peptides of charge 2 (one N-terminal and one at tryptic C-terminal R or K residue) should be dominant. Ionization issues (voltage?), in-source fragmentation, missed cleavages and buffer irregularities can cause a shift (see Bittremieux 2017, DOI: 10.1002/mas.21544 ).
        • Heatmap score [MC]: the fraction (0% - 100%) of fully cleaved peptides per Raw file
        • Heatmap score [MC Var]: each Raw file is scored for its deviation from the ‘average’ digestion state of the current study.
        • Heatmap score [ID rate over RT]: Judge column occupancy over retention time. Ideally, the LC gradient is chosen such that the number of identifications (here, after FDR filtering) is uniform over time, to ensure consistent instrument duty cycles. Sharp peaks and uneven distribution of identifications over time indicate potential for LC gradient optimization.Scored using ‘Uniform’ scoring function. i.e. constant receives good score, extreme shapes are bad.
        • Heatmap score [MS2 Oversampling]: The percentage of non-oversampled 3D-peaks. An oversampled 3D-peak is defined as a peak whose peptide ion (same sequence and same charge state) was identified by at least two distinct MS2 spectra in the same Raw file. For high complexity samples, oversampling of individual 3D-peaks automatically leads to undersampling or even omission of other 3D-peaks, reducing the number of identified peptides.
        • Heatmap score [Pep Missing]: Linear scale of the fraction of missing peptides.
        loading..

        Summary Table

        This plot shows the quantms pipeline summary statistics

        This plot shows the quantms pipeline summary statistics

        Showing 1/1 rows and 5/5 columns.
        Total MS/MS SpectralTotal MS/MS Spectral IdentifiedIdentified MS/MS Spectral CoverageTotal Peptide CountTotal Protein IdentifiedTotal Protein Quantified
        6103
        1592
        26.09%
        1232
        556
        478

        Pipeline Result Statistics

        This plot shows the quantms pipeline final result

        This plot shows the quantms pipeline final result. Including Sample Name、Possible Study Variables、identified the number of peptide in the pipeline、 and identified the number of modified peptide in the pipeline, eg. All data in this table are obtained from the out_msstats file. You can also remove the decoy with the remove_decoy parameter.

        Showing 1/1 rows and 7/7 columns.
        Spectra FileSample Nameconditionfractionpeptide_numunique_peptide_nummodified_peptide_numprotein_num
        TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzML
        1|2|3|4|5|6
        SP=Yeast;CT=protein;AC=P00924;QY=10|SP=BOVIN;CT=protein;AC=P02769;QY=1|SP=RABIT;CT=protein;AC=P00489;QY=2|SP=BOVIN;CT=protein;AC=P62894;QY=1SP=Yeast;CT=protein;AC=P00924;QY=5|SP=BOVIN;CT=protein;AC=P02769;QY=2.5|SP=RABIT;CT=protein;AC=P00489;QY=2|SP=BOVIN;CT=protein;AC=P62894;QY=1SP=Yeast;CT=protein;AC=P00924;QY=2.5|SP=BOVIN;CT=protein;AC=P02769;QY=5|SP=RABIT;CT=protein;AC=P00489;QY=2|SP=BOVIN;CT=protein;AC=P62894;QY=1SP=Yeast;CT=protein;AC=P00924;QY=1|SP=BOVIN;CT=protein;AC=P02769;QY=10|SP=RABIT;CT=protein;AC=P00489;QY=2|SP=BOVIN;CT=protein;AC=P62894;QY=1SP=Yeast;CT=protein;AC=P00924;QY=2.5|SP=BOVIN;CT=protein;AC=P02769;QY=5|SP=RABIT;CT=protein;AC=P00489;QY=1|SP=BOVIN;CT=protein;AC=P62894;QY=1SP=Yeast;CT=protein;AC=P00924;QY=10|SP=BOVIN;CT=protein;AC=P02769;QY=1|SP=RABIT;CT=protein;AC=P00489;QY=1|SP=BOVIN;CT=protein;AC=P62894;QY=2
        1
        1263
        1232
        1263
        550

        Number of Peptides Per Proteins

        This plot shows the number of peptides per proteins in quantms pipeline final result

        This statistic is extracted from the out_msstats file. Proteins supported by more peptide identifications can constitute more confident results.

        loading..

        Quantification Result

        This plot shows the quantification information of peptidesin quantms pipeline final result

        The quantification information of peptides is obtained from the pep table in the mzTab file. The table shows the quantitative level of peptides in different study variables.

        Showing 50/50 rows and 7/7 columns.
        IDsequencepeptide_abundance_study_variable[1]peptide_abundance_study_variable[2]peptide_abundance_study_variable[3]peptide_abundance_study_variable[4]peptide_abundance_study_variable[5]peptide_abundance_study_variable[6]
        1
        THSQEEMQHMQR
        1735.085083
        0.000000
        1943.624390
        0.000000
        2206.387695
        2464.229004
        2
        RHDDAGK
        19410.570312
        0.000000
        28630.349609
        0.000000
        23973.830078
        23542.150391
        3
        DRPGHDMR
        17882.259766
        0.000000
        19202.419922
        0.000000
        17539.250000
        17832.869141
        4
        VEHTSQGAK
        60083.011719
        0.000000
        57753.839844
        0.000000
        29032.550781
        36485.621094
        5
        EMRDPSQEDSR
        84316.390625
        0.000000
        115507.101562
        0.000000
        104554.703125
        88389.859375
        6
        RPADKDR
        36883.648438
        0.000000
        43626.601562
        0.000000
        33929.769531
        38362.968750
        7
        GAHAGEQVAR
        26513.839844
        0.000000
        30474.480469
        0.000000
        23655.339844
        28565.949219
        8
        KAHHLENNPR
        18142.929688
        0.000000
        24134.089844
        0.000000
        21253.679688
        20231.619141
        9
        HTDGETGVGR
        30000.759766
        0.000000
        30018.759766
        0.000000
        28804.630859
        26815.890625
        10
        EQSSDRK
        100622.796875
        0.000000
        120346.796875
        0.000000
        109587.898438
        107271.500000
        11
        HTDGETGVGR
        13051.750000
        0.000000
        14966.000000
        0.000000
        13260.929688
        14091.429688
        12
        REEEASAQQQNAER
        68758.742188
        0.000000
        82591.148438
        0.000000
        70141.132812
        85951.789062
        13
        ESTPAQR
        187654.703125
        0.000000
        213893.906250
        0.000000
        172164.906250
        185784.703125
        14
        AKDENQR
        35554.898438
        0.000000
        43427.371094
        0.000000
        37197.429688
        40482.929688
        15
        KMTHGNAQEADAAR
        18001.250000
        0.000000
        38090.378906
        0.000000
        22232.300781
        17518.919922
        16
        KSEESGR
        29720.679688
        0.000000
        37083.378906
        0.000000
        29194.060547
        33036.820312
        17
        NATEGTDAR
        40999.750000
        0.000000
        50422.691406
        0.000000
        40769.941406
        39942.261719
        18
        KNDEDR
        21855.480469
        0.000000
        25656.099609
        0.000000
        21711.710938
        21680.919922
        19
        HAVVHNQK
        204801.406250
        0.000000
        230682.703125
        0.000000
        188799.296875
        220652.703125
        20
        TQDATHGNSLSHR
        30774.230469
        0.000000
        43666.609375
        0.000000
        42708.500000
        42587.699219
        21
        TGNTPDGR
        191922.000000
        0.000000
        229716.703125
        0.000000
        189698.000000
        181597.203125
        22
        THSQEEMQHMQR
        12017.400391
        0.000000
        14852.809570
        0.000000
        12186.950195
        14125.259766
        23
        TQDATHGNSLSHR
        42538.839844
        0.000000
        60692.558594
        0.000000
        51752.750000
        47865.960938
        24
        RNEEAK
        281165.687500
        0.000000
        352188.906250
        0.000000
        288759.093750
        291013.312500
        25
        AATAEDR
        66683.453125
        0.000000
        78517.648438
        0.000000
        68246.960938
        70666.421875
        26
        AQGEDPR
        23652.179688
        0.000000
        28578.150391
        0.000000
        24918.060547
        27503.050781
        27
        EAEEQAQR
        59177.980469
        0.000000
        66066.406250
        0.000000
        57256.140625
        66176.898438
        28
        RGESSGPDVSR
        80917.343750
        0.000000
        108254.203125
        0.000000
        98951.078125
        94877.953125
        29
        DPSQEDSR
        349113.093750
        0.000000
        395046.500000
        0.000000
        286556.187500
        369059.500000
        30
        RNEEAK
        90528.750000
        0.000000
        115590.101562
        0.000000
        99124.500000
        98502.820312
        31
        DRGHEVK
        42410.199219
        0.000000
        55092.769531
        0.000000
        50626.781250
        49205.371094
        32
        EHVEHAK
        317741.906250
        0.000000
        391086.093750
        0.000000
        351391.000000
        371309.406250
        33
        ATNPANR
        116389.296875
        0.000000
        130693.000000
        0.000000
        113060.601562
        117136.296875
        34
        HEMQDTAK
        85331.226563
        0.000000
        121460.398438
        0.000000
        91157.578125
        90450.640625
        35
        HELDDERR
        38871.761719
        0.000000
        43595.671875
        0.000000
        39551.328125
        43151.550781
        36
        HELDDERR
        17346.259766
        0.000000
        20486.560547
        0.000000
        17362.410156
        17356.869141
        37
        GTAMNPVDHPHGGGEGR
        8526.269531
        0.000000
        9824.986328
        0.000000
        8300.170898
        9177.175781
        38
        PQDHSQK
        231763.500000
        0.000000
        302895.406250
        0.000000
        253176.703125
        231796.000000
        39
        SLQSQEK
        126669.601562
        0.000000
        121184.500000
        0.000000
        117991.601562
        184021.906250
        40
        GTAMNPVDHPHGGGEGR
        20678.699219
        0.000000
        24083.869141
        0.000000
        17320.939453
        20790.009766
        41
        DATAEAQR
        61261.371094
        0.000000
        67691.406250
        0.000000
        62567.109375
        62275.761719
        42
        EGPAEDGSNR
        722895.000000
        0.000000
        806872.500000
        0.000000
        653708.187500
        781531.500000
        43
        QPADGPDR
        313855.093750
        0.000000
        316842.906250
        0.000000
        251429.703125
        324143.500000
        44
        DPDGEER
        268776.593750
        0.000000
        297178.687500
        0.000000
        239360.406250
        281645.187500
        45
        KDDNHYR
        80956.960938
        0.000000
        87266.351562
        0.000000
        66358.140625
        85902.062500
        46
        QMVSHK
        226039.906250
        0.000000
        264303.687500
        0.000000
        236738.406250
        218760.406250
        47
        GPTPDR
        119354.296875
        0.000000
        140637.406250
        0.000000
        132822.593750
        142817.203125
        48
        GHAADKK
        1390683.000000
        0.000000
        1548275.000000
        0.000000
        1391920.000000
        1499376.000000
        49
        AEAEAER
        339436.000000
        0.000000
        370886.500000
        0.000000
        331869.687500
        354285.687500
        50
        AQQADQE
        208847.000000
        0.000000
        221854.000000
        0.000000
        192225.203125
        216008.593750
        First Page Previous PageNext Page Last PagePage/Total Pages

        Peptide-Spectrum Matches

        This plot shows the PSM informationin quantms pipeline final result

        This table fully displays the peptide spectrum matching information in the mzTab file:

        • sequence: peptide sequence
        • unique
        • search_engine_score
        Showing 50/50 rows and 3/3 columns.
        PSM_IDsequenceuniquesearch_engine_score[1]
        0
        THSQEEMQHMQR
        1
        0.000000000
        1
        RHDDAGK
        1
        0.000000000
        2
        DRPGHDMR
        1
        0.000000000
        3
        VEHTSQGAK
        1
        0.000694927
        4
        EMRDPSQEDSR
        1
        0.000000000
        5
        RPADKDR
        1
        0.001374570
        6
        GAHAGEQVAR
        1
        0.000000000
        7
        KAHHLENNPR
        1
        0.000000000
        8
        HTDGETGVGR
        1
        0.000000000
        9
        EQSSDRK
        1
        0.000000000
        10
        HTDGETGVGR
        1
        0.000000000
        11
        REEEASAQQQNAER
        1
        0.000000000
        12
        ESTPAQR
        1
        0.000000000
        13
        AKDENQR
        1
        0.000000000
        14
        KMTHGNAQEADAAR
        1
        0.000000000
        15
        KSEESGR
        1
        0.000694927
        16
        NATEGTDAR
        1
        0.000000000
        17
        KNDEDR
        1
        0.002717391
        18
        HAVVHNQK
        1
        0.000000000
        19
        TQDATHGNSLSHR
        1
        0.000000000
        20
        TGNTPDGR
        1
        0.000000000
        21
        THSQEEMQHMQR
        1
        0.000000000
        22
        TQDATHGNSLSHR
        1
        0.000000000
        23
        RNEEAK
        0
        0.000000000
        24
        AATAEDR
        1
        0.000000000
        25
        AQGEDPR
        1
        0.000694927
        26
        EAEEQAQR
        1
        0.000000000
        27
        RGESSGPDVSR
        1
        0.000000000
        28
        DPSQEDSR
        1
        0.000000000
        29
        RNEEAK
        0
        0.004062288
        30
        DRGHEVK
        1
        0.004713805
        31
        EHVEHAK
        1
        0.000000000
        32
        ATNPANR
        1
        0.000000000
        33
        HEMQDTAK
        1
        0.000000000
        34
        HELDDERR
        1
        0.000000000
        35
        HELDDERR
        1
        0.000694927
        36
        GTAMNPVDHPHGGGEGR
        1
        0.000000000
        37
        PQDHSQK
        1
        0.000000000
        38
        SLQSQEK
        1
        0.000694927
        39
        GTAMNPVDHPHGGGEGR
        1
        0.000000000
        40
        DATAEAQR
        1
        0.000000000
        41
        EGPAEDGSNR
        1
        0.000000000
        42
        QPADGPDR
        1
        0.000000000
        43
        DPDGEER
        1
        0.000000000
        44
        KDDNHYR
        1
        0.000694927
        45
        QMVSHK
        1
        0.000000000
        46
        GPTPDR
        1
        0.001374570
        47
        GHAADKK
        1
        0.000694927
        48
        AEAEAER
        1
        0.000000000
        49
        AQQADQE
        1
        0.000000000
        First Page Previous PageNext Page Last PagePage/Total Pages

        Spectra Tracking

        This plot shows the quantms pipeline mzML tracking

        This table shows the changes in the number of spectra corresponding to each input file during the pipeline operation. And the number of peptides finally identified is obtained from the PSM table in the mzTab file. You can also remove the decoy with the remove_decoy parameter.:

        • MS1_Num: The number of MS1 spectra extracted from mzMLs
        • MS2_Num: The number of MS2 spectra extracted from mzMLs
        • MSGF: The Number of spectra identified by MSGF search engine
        • Comet: The Number of spectra identified by Comet search engine
        • Final result of spectra: extracted from PSM table in mzTab file
        • Final result of Peptides: extracted from PSM table in mzTab file
        Showing 1/1 rows and 6/6 columns.
        Spectra FileMS1_NumMS2_NumFinal result of spectraCometFinal result of PeptidesMSGF
        TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzML
        1431
        6103
        1592
        3309
        1232
        3104

        Distribution of precursor charges

        This is a bar chart representing the distribution of the precursor ion charges for a given whole experiment.

        This information can be used to identify potential ionization problems including many 1+ charges from an ESI ionization source or an unexpected distribution of charges. MALDI experiments are expected to contain almost exclusively 1+ charged ions. An unexpected charge distribution may furthermore be caused by specific search engine parameter settings such as limiting the search to specific ion charges.

        loading..

        Number of Peaks per MS/MS spectrum

        This chart represents a histogram containing the number of peaks per MS/MS spectrum in a given experiment. This chart assumes centroid data. Too few peaks can identify poor fragmentation or a detector fault, as opposed to a large number of peaks representing very noisy spectra. This chart is extensively dependent on the pre-processing steps performed to the spectra (centroiding, deconvolution, peak picking approach, etc).

        loading..

        Peak Intensity Distribution

        This is a histogram representing the ion intensity vs. the frequency for all MS2 spectra in a whole given experiment. It is possible to filter the information for all, identified and unidentified spectra. This plot can give a general estimation of the noise level of the spectra.

        Generally, one should expect to have a high number of low intensity noise peaks with a low number of high intensity signal peaks. A disproportionate number of high signal peaks may indicate heavy spectrum pre-filtering or potential experimental problems. In the case of data reuse this plot can be useful in identifying the requirement for pre-processing of the spectra prior to any downstream analysis. The quality of the identifications is not linked to this data as most search engines perform internal spectrum pre-processing before matching the spectra. Thus, the spectra reported are not necessarily pre-processed since the search engine may have applied the pre-processing step internally. This pre-processing is not necessarily reported in the experimental metadata.

        loading..

        Oversampling Distribution

        An oversampled 3D-peak is defined as a peak whose peptide ion (same sequence and same charge state) was identified by at least two distinct MS2 spectra in the same Raw file.

        For high complexity samples, oversampling of individual 3D-peaks automatically leads to undersampling or even omission of other 3D-peaks, reducing the number of identified peptides. Oversampling occurs in low-complexity samples or long LC gradients, as well as undersized dynamic exclusion windows for data independent acquisitions.

                    * Heatmap score [EVD: MS2 Oversampling]: The percentage of non-oversampled 3D-peaks.
        
        loading..